Overview

Dataset statistics

Number of variables13
Number of observations2968
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory290.0 KiB
Average record size in memory100.0 B

Variable types

NUM13

Reproduction

Analysis started2022-09-26 20:15:40.082226
Analysis finished2022-09-26 20:16:08.031638
Versionpandas-profiling v2.6.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml
qtde_items is highly correlated with gross_revenueHigh Correlation
gross_revenue is highly correlated with qtde_itemsHigh Correlation
avg_ticket is highly skewed (γ1 = 25.1569664) Skewed
frequency is highly skewed (γ1 = 24.87687084) Skewed
qtde_returns is highly skewed (γ1 = 21.9754032) Skewed
recency_days has 33 (1.1%) zeros Zeros
qtde_returns has 1481 (49.9%) zeros Zeros

Variables

df_index
Real number (ℝ≥0)

UNIQUE
Distinct count2968
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2316.6664420485176
Minimum0
Maximum5714
Zeros1
Zeros (%)< 0.1%
Memory size23.3 KiB
2022-09-26T22:16:08.095744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.35
Q1928.5
median2119.5
Q33536.25
95-th percentile5034.3
Maximum5714
Range5714
Interquartile range (IQR)2607.75

Descriptive statistics

Standard deviation1554.722712
Coefficient of variation (CV)0.6711033938
Kurtosis-1.010637904
Mean2316.666442
Median Absolute Deviation (MAD)1329.105777
Skewness0.3426249769
Sum6875866
Variance2417162.71
2022-09-26T22:16:08.177628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2022-09-26T22:16:08.278628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with variable size bins (bins=[ 0. 979.5 2918.5 4826.5 5413. 5714. ], "bayesian blocks" binning strategy used)
ValueCountFrequency (%) 
0 1 < 0.1%
 
3010 1 < 0.1%
 
2995 1 < 0.1%
 
2996 1 < 0.1%
 
2999 1 < 0.1%
 
3000 1 < 0.1%
 
3001 1 < 0.1%
 
3002 1 < 0.1%
 
3005 1 < 0.1%
 
3007 1 < 0.1%
 
Other values (2958) 2958 99.7%
 
ValueCountFrequency (%) 
0 1 < 0.1%
 
1 1 < 0.1%
 
2 1 < 0.1%
 
3 1 < 0.1%
 
4 1 < 0.1%
 
ValueCountFrequency (%) 
5714 1 < 0.1%
 
5695 1 < 0.1%
 
5685 1 < 0.1%
 
5679 1 < 0.1%
 
5658 1 < 0.1%
 

customer_id
Real number (ℝ≥0)

UNIQUE
Distinct count2968
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.377021563343
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Memory size11.7 KiB
2022-09-26T22:16:08.366681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.35
Q113798.75
median15220.5
Q316768.5
95-th percentile17964.65
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.144523
Coefficient of variation (CV)0.1125803587
Kurtosis-1.206178196
Mean15270.37702
Median Absolute Deviation (MAD)1491.723924
Skewness0.03219371129
Sum45322479
Variance2955457.892
2022-09-26T22:16:08.446761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2022-09-26T22:16:08.545781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with variable size bins (bins=[12347. 18287.], "bayesian blocks" binning strategy used)
ValueCountFrequency (%) 
17850 1 < 0.1%
 
12670 1 < 0.1%
 
17734 1 < 0.1%
 
14905 1 < 0.1%
 
16103 1 < 0.1%
 
14626 1 < 0.1%
 
14868 1 < 0.1%
 
18246 1 < 0.1%
 
17115 1 < 0.1%
 
16611 1 < 0.1%
 
Other values (2958) 2958 99.7%
 
ValueCountFrequency (%) 
12347 1 < 0.1%
 
12348 1 < 0.1%
 
12352 1 < 0.1%
 
12356 1 < 0.1%
 
12358 1 < 0.1%
 
ValueCountFrequency (%) 
18287 1 < 0.1%
 
18283 1 < 0.1%
 
18282 1 < 0.1%
 
18277 1 < 0.1%
 
18276 1 < 0.1%
 

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
Distinct count2953
Unique (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.4850606469
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Memory size23.3 KiB
2022-09-26T22:16:08.630854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.7325
Q1570.845
median1085.51
Q32306.905
95-th percentile7169.562
Maximum279138.02
Range279131.82
Interquartile range (IQR)1736.06

Descriptive statistics

Standard deviation10135.46528
Coefficient of variation (CV)3.762955818
Kurtosis397.3013221
Mean2693.485061
Median Absolute Deviation (MAD)2686.593075
Skewness17.63537227
Sum7994263.66
Variance102727656.5
2022-09-26T22:16:08.706200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2022-09-26T22:16:08.811977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with variable size bins (bins=[6.2000000e+00 8.9750000e+01 2.2571500e+02 7.3530000e+02 1.1431300e+03 ... 6.9505750e+03 1.1164310e+04 1.7539295e+04 6.9767825e+04 2.7913802e+05], "bayesian blocks" binning strategy used)
ValueCountFrequency (%) 
1078.96 2 0.1%
 
2053.02 2 0.1%
 
331 2 0.1%
 
1353.74 2 0.1%
 
889.93 2 0.1%
 
745.06 2 0.1%
 
379.65 2 0.1%
 
2092.32 2 0.1%
 
731.9 2 0.1%
 
734.94 2 0.1%
 
Other values (2943) 2948 99.3%
 
ValueCountFrequency (%) 
6.2 1 < 0.1%
 
13.3 1 < 0.1%
 
15 1 < 0.1%
 
36.56 1 < 0.1%
 
45 1 < 0.1%
 
ValueCountFrequency (%) 
279138.02 1 < 0.1%
 
259657.3 1 < 0.1%
 
194550.79 1 < 0.1%
 
140450.72 1 < 0.1%
 
124564.53 1 < 0.1%
 

recency_days
Real number (ℝ≥0)

ZEROS
Distinct count272
Unique (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.30929919137466
Minimum0.0
Maximum373.0
Zeros33
Zeros (%)1.1%
Memory size23.3 KiB
2022-09-26T22:16:08.895881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.76092244
Coefficient of variation (CV)1.209170733
Kurtosis2.776517247
Mean64.30929919
Median Absolute Deviation (MAD)57.66831831
Skewness1.798052889
Sum190870
Variance6046.761059
2022-09-26T22:16:08.976851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2022-09-26T22:16:09.095058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with variable size bins (bins=[ 0. 4.5 7.5 11.5 14.5 ... 116.5 219.5 336.5 356. 373. ], "bayesian blocks" binning strategy used)
ValueCountFrequency (%) 
1 99 3.3%
 
4 87 2.9%
 
2 85 2.9%
 
3 85 2.9%
 
8 76 2.6%
 
10 67 2.3%
 
9 66 2.2%
 
7 66 2.2%
 
17 64 2.2%
 
16 55 1.9%
 
Other values (262) 2218 74.7%
 
ValueCountFrequency (%) 
0 33 1.1%
 
1 99 3.3%
 
2 85 2.9%
 
3 85 2.9%
 
4 87 2.9%
 
ValueCountFrequency (%) 
373 2 0.1%
 
372 4 0.1%
 
371 1 < 0.1%
 
368 1 < 0.1%
 
366 4 0.1%
 

qtde_invoices
Real number (ℝ≥0)

Distinct count56
Unique (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.724393530997305
Minimum1.0
Maximum206.0
Zeros0
Zeros (%)0.0%
Memory size23.3 KiB
2022-09-26T22:16:09.222829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.857759893
Coefficient of variation (CV)1.547370886
Kurtosis190.7862392
Mean5.724393531
Median Absolute Deviation (MAD)4.06091617
Skewness10.76555481
Sum16990
Variance78.45991032
2022-09-26T22:16:09.299875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2022-09-26T22:16:09.401867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with variable size bins (bins=[ 1. 1.5 2.5 3.5 4.5 ... 21.5 28.5 38. 67. 206. ], "bayesian blocks" binning strategy used)
ValueCountFrequency (%) 
2 784 26.4%
 
3 499 16.8%
 
4 393 13.2%
 
5 237 8.0%
 
1 190 6.4%
 
6 173 5.8%
 
7 138 4.6%
 
8 98 3.3%
 
9 69 2.3%
 
10 55 1.9%
 
Other values (46) 332 11.2%
 
ValueCountFrequency (%) 
1 190 6.4%
 
2 784 26.4%
 
3 499 16.8%
 
4 393 13.2%
 
5 237 8.0%
 
ValueCountFrequency (%) 
206 1 < 0.1%
 
199 1 < 0.1%
 
124 1 < 0.1%
 
97 1 < 0.1%
 
91 2 0.1%
 

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION
Distinct count1670
Unique (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1582.1044474393532
Minimum1.0
Maximum196844.0
Zeros0
Zeros (%)0.0%
Memory size23.3 KiB
2022-09-26T22:16:09.491901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.35
Q1296
median640
Q31399.5
95-th percentile4403.25
Maximum196844
Range196843
Interquartile range (IQR)1103.5

Descriptive statistics

Standard deviation5705.291445
Coefficient of variation (CV)3.60614083
Kurtosis516.7418024
Mean1582.104447
Median Absolute Deviation (MAD)1586.981487
Skewness18.73765362
Sum4695686
Variance32550350.48
2022-09-26T22:16:09.835056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2022-09-26T22:16:09.968825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with variable size bins (bins=[1.00000e+00 7.25000e+01 3.81500e+02 6.12500e+02 1.04150e+03 ... 6.20400e+03 1.14660e+04 3.32240e+04 7.88180e+04 1.96844e+05], "bayesian blocks" binning strategy used)
ValueCountFrequency (%) 
310 11 0.4%
 
150 9 0.3%
 
88 9 0.3%
 
246 8 0.3%
 
272 8 0.3%
 
84 8 0.3%
 
260 8 0.3%
 
288 8 0.3%
 
1200 7 0.2%
 
516 7 0.2%
 
Other values (1660) 2885 97.2%
 
ValueCountFrequency (%) 
1 1 < 0.1%
 
2 2 0.1%
 
12 2 0.1%
 
16 1 < 0.1%
 
17 1 < 0.1%
 
ValueCountFrequency (%) 
196844 1 < 0.1%
 
80263 1 < 0.1%
 
77373 1 < 0.1%
 
69993 1 < 0.1%
 
64549 1 < 0.1%
 

qtde_products
Real number (ℝ≥0)

Distinct count468
Unique (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.76448787061994
Minimum1.0
Maximum7838.0
Zeros0
Zeros (%)0.0%
Memory size23.3 KiB
2022-09-26T22:16:10.078572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.9329358
Coefficient of variation (CV)2.198786803
Kurtosis354.7788412
Mean122.7644879
Median Absolute Deviation (MAD)104.8061719
Skewness15.7061352
Sum364365
Variance72863.78981
2022-09-26T22:16:10.169676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2022-09-26T22:16:10.304888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with variable size bins (bins=[1.0000e+00 4.5000e+00 3.5500e+01 5.3500e+01 8.7500e+01 ... 4.8100e+02 7.1650e+02 1.1715e+03 1.9390e+03 7.8380e+03], "bayesian blocks" binning strategy used)
ValueCountFrequency (%) 
28 43 1.4%
 
20 37 1.2%
 
35 35 1.2%
 
29 35 1.2%
 
19 34 1.1%
 
15 33 1.1%
 
11 32 1.1%
 
26 31 1.0%
 
27 30 1.0%
 
25 30 1.0%
 
Other values (458) 2628 88.5%
 
ValueCountFrequency (%) 
1 6 0.2%
 
2 14 0.5%
 
3 15 0.5%
 
4 17 0.6%
 
5 26 0.9%
 
ValueCountFrequency (%) 
7838 1 < 0.1%
 
5673 1 < 0.1%
 
5095 1 < 0.1%
 
4580 1 < 0.1%
 
2698 1 < 0.1%
 

avg_ticket
Real number (ℝ≥0)

SKEWED
Distinct count2965
Unique (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.9942567139799
Minimum2.150588235294118
Maximum4453.43
Zeros0
Zeros (%)0.0%
Memory size23.3 KiB
2022-09-26T22:16:10.410005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.915887985
Q113.11811111
median17.95344712
Q324.98179365
95-th percentile90.052125
Maximum4453.43
Range4451.279412
Interquartile range (IQR)11.86368254

Descriptive statistics

Standard deviation119.5320656
Coefficient of variation (CV)3.622814318
Kurtosis812.9647397
Mean32.99425671
Median Absolute Deviation (MAD)28.22915
Skewness25.1569664
Sum97926.95393
Variance14287.91471
2022-09-26T22:16:10.487021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2022-09-26T22:16:10.588200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with variable size bins (bins=[2.15058824e+00 3.41143241e+00 9.23139562e+00 1.36831588e+01 1.49940874e+01 ... 9.73690110e+01 1.80263353e+02 2.98707656e+02 6.45584167e+02 4.45343000e+03], "bayesian blocks" binning strategy used)
ValueCountFrequency (%) 
15 2 0.1%
 
4.162 2 0.1%
 
14.47833333 2 0.1%
 
18.15222222 1 < 0.1%
 
13.92736842 1 < 0.1%
 
36.24411765 1 < 0.1%
 
29.78416667 1 < 0.1%
 
22.8792623 1 < 0.1%
 
20.51104167 1 < 0.1%
 
149.025 1 < 0.1%
 
Other values (2955) 2955 99.6%
 
ValueCountFrequency (%) 
2.150588235 1 < 0.1%
 
2.4325 1 < 0.1%
 
2.462371134 1 < 0.1%
 
2.511241379 1 < 0.1%
 
2.515333333 1 < 0.1%
 
ValueCountFrequency (%) 
4453.43 1 < 0.1%
 
3202.92 1 < 0.1%
 
1687.2 1 < 0.1%
 
952.9875 1 < 0.1%
 
872.13 1 < 0.1%
 

avg_recency_days
Real number (ℝ)

Distinct count1258
Unique (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-67.30213284742761
Minimum-366.0
Maximum-1.0
Zeros0
Zeros (%)0.0%
Memory size23.3 KiB
2022-09-26T22:16:10.675136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-366
5-th percentile-200.65
Q1-85.33333333
median-48.26785714
Q3-25.91730769
95-th percentile-8
Maximum-1
Range365
Interquartile range (IQR)59.41602564

Descriptive statistics

Standard deviation63.50535844
Coefficient of variation (CV)-0.9435861206
Kurtosis4.908048776
Mean-67.30213285
Median Absolute Deviation (MAD)44.9017599
Skewness-2.066084007
Sum-199752.7303
Variance4032.93055
2022-09-26T22:16:10.758811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2022-09-26T22:16:10.903417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with variable size bins (bins=[-366. -261. -184.5 -126.25 -97.16666667 ... -4.92857143 -4.07222222 -3.75 -1.25 -1. ], "bayesian blocks" binning strategy used)
ValueCountFrequency (%) 
-14 25 0.8%
 
-4 22 0.7%
 
-70 21 0.7%
 
-7 20 0.7%
 
-35 19 0.6%
 
-49 18 0.6%
 
-11 17 0.6%
 
-46 17 0.6%
 
-21 17 0.6%
 
-28 16 0.5%
 
Other values (1248) 2776 93.5%
 
ValueCountFrequency (%) 
-366 1 < 0.1%
 
-365 1 < 0.1%
 
-363 1 < 0.1%
 
-362 1 < 0.1%
 
-357 2 0.1%
 
ValueCountFrequency (%) 
-1 16 0.5%
 
-1.5 1 < 0.1%
 
-2 13 0.4%
 
-2.5 1 < 0.1%
 
-2.601398601 1 < 0.1%
 

frequency
Real number (ℝ≥0)

SKEWED
Distinct count1225
Unique (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11383237420201338
Minimum0.005449591280653951
Maximum17.0
Zeros0
Zeros (%)0.0%
Memory size23.3 KiB
2022-09-26T22:16:11.018077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008893504781
Q10.01633986928
median0.02589835169
Q30.04947858264
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.03313871336

Descriptive statistics

Standard deviation0.4082205551
Coefficient of variation (CV)3.586155151
Kurtosis989.0663249
Mean0.1138323742
Median Absolute Deviation (MAD)0.1485900171
Skewness24.87687084
Sum337.8544866
Variance0.1666440216
2022-09-26T22:16:11.105843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2022-09-26T22:16:11.238251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with variable size bins (bins=[5.44959128e-03 7.67757147e-03 1.05726954e-02 2.20791355e-02 2.77350427e-02 ... 2.56793478e-01 8.75000000e-01 1.07142857e+00 2.50000000e+00 1.70000000e+01], "bayesian blocks" binning strategy used)
ValueCountFrequency (%) 
1 198 6.7%
 
0.0625 18 0.6%
 
0.02777777778 17 0.6%
 
0.02380952381 16 0.5%
 
0.09090909091 15 0.5%
 
0.08333333333 15 0.5%
 
0.03448275862 14 0.5%
 
0.02941176471 14 0.5%
 
0.03571428571 13 0.4%
 
0.07692307692 13 0.4%
 
Other values (1215) 2635 88.8%
 
ValueCountFrequency (%) 
0.005449591281 1 < 0.1%
 
0.005464480874 1 < 0.1%
 
0.005479452055 1 < 0.1%
 
0.005494505495 1 < 0.1%
 
0.005586592179 2 0.1%
 
ValueCountFrequency (%) 
17 1 < 0.1%
 
3 1 < 0.1%
 
2 6 0.2%
 
1.142857143 1 < 0.1%
 
1 198 6.7%
 

qtde_returns
Real number (ℝ≥0)

SKEWED
ZEROS
Distinct count213
Unique (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.88847708894879
Minimum0.0
Maximum9014.0
Zeros1481
Zeros (%)49.9%
Memory size23.3 KiB
2022-09-26T22:16:11.342977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation282.864784
Coefficient of variation (CV)8.107685048
Kurtosis596.2019916
Mean34.88847709
Median Absolute Deviation (MAD)55.57697947
Skewness21.9754032
Sum103549
Variance80012.48604
2022-09-26T22:16:11.415848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2022-09-26T22:16:11.517309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with variable size bins (bins=[0.000e+00 5.000e-01 2.500e+00 6.500e+00 1.250e+01 ... 1.245e+02 2.110e+02 4.660e+02 2.017e+03 9.014e+03], "bayesian blocks" binning strategy used)
ValueCountFrequency (%) 
0 1481 49.9%
 
1 164 5.5%
 
2 148 5.0%
 
3 105 3.5%
 
4 89 3.0%
 
6 78 2.6%
 
5 61 2.1%
 
12 51 1.7%
 
7 43 1.4%
 
8 43 1.4%
 
Other values (203) 705 23.8%
 
ValueCountFrequency (%) 
0 1481 49.9%
 
1 164 5.5%
 
2 148 5.0%
 
3 105 3.5%
 
4 89 3.0%
 
ValueCountFrequency (%) 
9014 1 < 0.1%
 
8004 1 < 0.1%
 
4427 1 < 0.1%
 
3768 1 < 0.1%
 
3332 1 < 0.1%
 

avg_basket_size
Real number (ℝ≥0)

Distinct count1978
Unique (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.25288600864948
Minimum1.0
Maximum6009.333333333333
Zeros0
Zeros (%)0.0%
Memory size23.3 KiB
2022-09-26T22:16:11.608070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.2375
median172.2916667
Q3281.5480769
95-th percentile599.58
Maximum6009.333333
Range6008.333333
Interquartile range (IQR)178.3105769

Descriptive statistics

Standard deviation283.8931966
Coefficient of variation (CV)1.201649645
Kurtosis102.7816879
Mean236.252886
Median Absolute Deviation (MAD)146.2036617
Skewness7.701877717
Sum701198.5657
Variance80595.34706
2022-09-26T22:16:11.686856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2022-09-26T22:16:11.804245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with variable size bins (bins=[1.00000000e+00 2.21000000e+01 3.58750000e+01 7.24500000e+01 1.44583333e+02 ... 4.44544118e+02 6.62931373e+02 1.08693889e+03 2.12127957e+03 6.00933333e+03], "bayesian blocks" binning strategy used)
ValueCountFrequency (%) 
100 11 0.4%
 
114 10 0.3%
 
82 9 0.3%
 
86 9 0.3%
 
73 9 0.3%
 
136 8 0.3%
 
75 8 0.3%
 
60 8 0.3%
 
88 8 0.3%
 
130 7 0.2%
 
Other values (1968) 2881 97.1%
 
ValueCountFrequency (%) 
1 2 0.1%
 
2 1 < 0.1%
 
3.333333333 1 < 0.1%
 
5.333333333 1 < 0.1%
 
5.666666667 1 < 0.1%
 
ValueCountFrequency (%) 
6009.333333 1 < 0.1%
 
4282 1 < 0.1%
 
3906 1 < 0.1%
 
3868.65 1 < 0.1%
 
2880 1 < 0.1%
 

avg_unique_basket_size
Real number (ℝ≥0)

Distinct count906
Unique (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.489977016887437
Minimum0.2
Maximum259.0
Zeros0
Zeros (%)0.0%
Memory size23.3 KiB
2022-09-26T22:16:11.916912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.666666667
median13.6
Q322.14464286
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.47797619

Descriptive statistics

Standard deviation15.46012684
Coefficient of variation (CV)0.8839420902
Kurtosis29.32468467
Mean17.48997702
Median Absolute Deviation (MAD)10.44434087
Skewness3.436467798
Sum51910.25179
Variance239.015522
2022-09-26T22:16:11.997695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2022-09-26T22:16:12.149290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with variable size bins (bins=[2.00000000e-01 9.73684211e-01 1.03571429e+00 1.99122807e+00 2.05555556e+00 ... 3.41428571e+01 4.86333333e+01 6.80000000e+01 1.04500000e+02 2.59000000e+02], "bayesian blocks" binning strategy used)
ValueCountFrequency (%) 
13 42 1.4%
 
9 41 1.4%
 
8 39 1.3%
 
16 39 1.3%
 
17 38 1.3%
 
14 38 1.3%
 
11 36 1.2%
 
5 36 1.2%
 
7 36 1.2%
 
15 35 1.2%
 
Other values (896) 2588 87.2%
 
ValueCountFrequency (%) 
0.2 1 < 0.1%
 
0.25 3 0.1%
 
0.3333333333 6 0.2%
 
0.4 1 < 0.1%
 
0.4090909091 1 < 0.1%
 
ValueCountFrequency (%) 
259 1 < 0.1%
 
177 1 < 0.1%
 
148 1 < 0.1%
 
127 1 < 0.1%
 
105 1 < 0.1%
 

Interactions

2022-09-26T22:15:47.550825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:47.807403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:47.916114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:48.024822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:48.137297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:48.242048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:48.358783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:48.472048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:48.578211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:48.689932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:48.799672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:48.903497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:49.012176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:49.117893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:49.227135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:49.335110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:49.442822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:49.554524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:49.669218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:49.786904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:49.902594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:50.005354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:50.118087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:50.231781image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:50.345062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:50.463747image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:50.583425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:50.699122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:50.815847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:50.922425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:51.031434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:51.144403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:51.270067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:51.387758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:51.490481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:51.715878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:51.825585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:51.927325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:52.036027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:52.143737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:52.253447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:52.379107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:52.493803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:52.609493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:52.718790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:52.839008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:52.957755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:53.067497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:53.195161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:53.312883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:53.424010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:53.536946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:53.652109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:53.759338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:53.863699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:53.965876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:54.069599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:54.165343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:54.275057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:54.382762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:54.485491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:54.592205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:54.696921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:54.793666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:54.896399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:54.994132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:55.107828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:55.225480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:55.341204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:55.457859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:55.568563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:55.692233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:55.813908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:55.937598image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:56.062245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:56.197891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:56.463859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:56.589523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:56.704721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:56.818417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:56.933111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:57.079721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:57.209405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:57.327091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:57.456713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:57.584372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:57.707045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:57.829717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:57.953386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:58.069077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:58.188757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:58.304449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:58.403185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:58.501921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:58.599660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:58.703383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:58.798130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:58.926786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:59.042477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:59.152186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:59.264912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:59.371603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:59.468350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:59.569080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:59.665823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:59.776566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:15:59.889310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:00.002017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:00.120699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:00.228412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:00.347611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:00.484757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:00.593976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:00.710176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:00.825866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:00.935574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:01.048272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:01.187899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:01.306592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:01.419831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:01.532491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:01.646793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:01.781403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:01.916042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:02.049659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:02.169343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:02.485499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:02.613158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:02.731861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:02.858502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:02.980177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:03.090883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:03.204578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:03.312291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:03.423636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:03.529353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:03.642086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:03.758769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:03.862463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:03.976160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:04.092848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:04.206545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:04.317249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:04.425958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:04.541648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:04.659335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:04.778896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:04.900572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:05.015852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:05.140522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:05.265189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:05.382875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:05.508539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:05.632209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:05.748897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:05.870573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:05.989268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:06.111927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:06.240206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:06.358890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:06.476575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:06.589392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:06.708075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:06.825761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:06.934499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:07.058141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:07.176824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:07.286531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:07.407208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-26T22:16:12.282933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-26T22:16:12.501173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-26T22:16:12.712185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-26T22:16:12.948596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Missing values

2022-09-26T22:16:07.638803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-26T22:16:07.886988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.01733.0297.018.152222-35.50000017.00000040.050.9705880.617647
11130473232.5956.09.01390.0171.018.904035-27.2500000.02830235.0154.44444411.666667
22125836705.382.015.05028.0232.028.902500-23.1875000.04032350.0335.2000007.600000
3313748948.2595.05.0439.028.033.866071-92.6666670.0179210.087.8000004.800000
4415100876.00333.03.080.03.0292.000000-8.6000000.07317122.026.6666670.333333
55152914623.3025.014.02102.0102.045.326471-23.2000000.04011529.0150.1428574.357143
66146885630.877.021.03621.0327.017.219786-18.3000000.057221399.0172.4285717.047619
77178095411.9116.012.02057.061.088.719836-35.7000000.03352041.0171.4166673.833333
881531160767.900.091.038194.02379.025.543464-4.1444440.243316474.0419.7142866.230769
99160982005.6387.07.0613.067.029.934776-47.6666670.0243900.087.5714294.857143

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
29585626177271060.2515.01.0645.066.016.064394-6.01.0000006.0645.00000066.000000
2959563617232421.522.02.0203.036.011.708889-12.00.1538460.0101.50000015.000000
2960563717468137.0010.02.0116.05.027.400000-4.00.4000000.058.0000002.500000
2961564813596697.045.02.0406.0166.04.199036-7.00.2500000.0203.00000066.500000
29625654148931237.859.02.0799.073.016.956849-2.00.6666670.0399.50000036.000000
2963565812479473.2011.01.0382.030.015.773333-4.01.00000034.0382.00000030.000000
2964567914126706.137.03.0508.015.047.075333-3.00.75000050.0169.3333334.666667
29655685135211092.391.03.0733.0435.02.511241-4.50.3000000.0244.333333104.000000
2966569515060301.848.04.0262.0120.02.515333-1.02.0000000.065.50000020.000000
2967571412558269.967.01.0196.011.024.541818-6.01.000000196.0196.00000011.000000